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arXiv · 2001.11547

HistomicsML2.0: Fast interactive machine learning for whole slide imaging data

Sanghoon Lee·Mohamed Amgad·Deepak R. Chittajallu·Matt McCormick·Brian P Pollack·Habiba Elfandy·Hagar Hussein·David A Gutman·Lee AD Cooper
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Abstract

Extracting quantitative phenotypic information from whole-slide images presents significant challenges for investigators who are not experienced in developing image analysis algorithms. We present new software that enables rapid learn-by-example training of machine learning classifiers for detection of histologic patterns in whole-slide imaging datasets. HistomicsML2.0 uses convolutional networks to be readily adaptable to a variety of applications, provides a web-based user interface, and is available as a software container to simplify deployment.

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Sanghoon Lee, Mohamed Amgad, Deepak R. Chittajallu, Matt McCormick, Brian P Pollack, Habiba Elfandy, Hagar Hussein, David A Gutman, Lee AD Cooper. 2020-01-30. HistomicsML2.0: Fast interactive machine learning for whole slide imaging data. https://arxiv.org/abs/2001.11547

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